




'''simple docstring'''


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case : List[str]  				=						16
_snake_case : int  				=						32
def 						snake_case_       (UpperCamelCase			:    Accelerator					,		UpperCamelCase			:    DatasetDict					,		UpperCamelCase			:    List[int]					,		UpperCamelCase			:    List[int]					,		UpperCamelCase			:    int = 16 ):





							'''simple docstring'''


							_a      	=							AutoTokenizer.from_pretrained('''bert-base-cased''' )
							_a      	=							DatasetDict(
							    {
							        '''train''': dataset['''train'''].select(UpperCamelCase ),
							        '''validation''': dataset['''train'''].select(UpperCamelCase ),
							        '''test''': dataset['''validation'''],
							    } )

							def tokenize_function(UpperCamelCase			:    List[str] ):
														# max_length=None => use the model max length (it's actually the default)
														_a      	=							tokenizer(examples['''sentence1''']					,		examples['''sentence2''']					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														_a      	=							datasets.map(
														    UpperCamelCase					,		batched=UpperCamelCase					,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,		)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							_a      	=							tokenized_datasets.rename_column('''label'''					,		'''labels''' )

							def collate_fn(UpperCamelCase			:    str ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_a      	=							128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					_a      	=							16
														elif accelerator.mixed_precision != "no":
																					_a      	=							8
														else:
																					_a      	=							None

														return tokenizer.pad(
														    UpperCamelCase					,		padding='''longest'''					,		max_length=UpperCamelCase					,		pad_to_multiple_of=UpperCamelCase					,		return_tensors='''pt'''					,		)

							# Instantiate dataloaders.
							_a      	=							DataLoader(
							    tokenized_datasets['''train''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )
							_a      	=							DataLoader(
							    tokenized_datasets['''validation''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )

							_a      	=							DataLoader(
							    tokenized_datasets['''test''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )

							return train_dataloader, eval_dataloader, test_dataloader
def 						snake_case_       (UpperCamelCase			:    Any					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							[]
							# Download the dataset
							_a      	=							load_dataset('''glue'''					,		'''mrpc''' )
							# Create our splits
							_a      	=							StratifiedKFold(n_splits=int(args.num_folds ) )
							# Initialize accelerator
							_a      	=							Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_a      	=							config['''lr''']
							_a      	=							int(config['''num_epochs'''] )
							_a      	=							int(config['''seed'''] )
							_a      	=							int(config['''batch_size'''] )

							_a      	=							evaluate.load('''glue'''					,		'''mrpc''' )

							# If the batch size is too big we use gradient accumulation
							_a      	=							1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_a      	=							batch_size // MAX_GPU_BATCH_SIZE
														_a      	=							MAX_GPU_BATCH_SIZE

							set_seed(UpperCamelCase )

							# New Code #
							# Create our folds:
							_a      	=							kfold.split(np.zeros(datasets['''train'''].num_rows )					,		datasets['''train''']['''label'''] )
							_a      	=							[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase ):
														_a	,				_a	,				_a      	=							get_fold_dataloaders(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_a      	=							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,		return_dict=UpperCamelCase )

														# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
														# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
														# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
														_a      	=							model.to(accelerator.device )

														# Instantiate optimizer
														_a      	=							AdamW(params=model.parameters()					,		lr=UpperCamelCase )

														# Instantiate scheduler
														_a      	=							get_linear_schedule_with_warmup(
														    optimizer=UpperCamelCase					,		num_warmup_steps=100					,		num_training_steps=(len(UpperCamelCase ) * num_epochs) // gradient_accumulation_steps					,		)

														# Prepare everything
														# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
														# prepare method.
														_a	,				_a	,				_a	,				_a	,				_a      	=							accelerator.prepare(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )

														# Now we train the model
														for epoch in range(UpperCamelCase ):
																					model.train()
																					for step, batch in enumerate(UpperCamelCase ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device )
																												_a      	=							model(**UpperCamelCase )
																												_a      	=							outputs.loss
																												_a      	=							loss / gradient_accumulation_steps
																												accelerator.backward(UpperCamelCase )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

																					model.eval()
																					for step, batch in enumerate(UpperCamelCase ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device )
																												with torch.no_grad():
																																			_a      	=							model(**UpperCamelCase )
																												_a      	=							outputs.logits.argmax(dim=-1 )
																												_a	,				_a      	=							accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																												metric.add_batch(
																												    predictions=UpperCamelCase					,		references=UpperCamelCase					,		)

																					_a      	=							metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(f'epoch {epoch}:'					,		UpperCamelCase )

														# New Code #
														# We also run predictions on the test set at the very end
														_a      	=							[]
														for step, batch in enumerate(UpperCamelCase ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device )
																					with torch.no_grad():
																												_a      	=							model(**UpperCamelCase )
																					_a      	=							outputs.logits
																					_a	,				_a      	=							accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																					fold_predictions.append(predictions.cpu() )
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(UpperCamelCase					,		dim=0 ) )
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							_a      	=							torch.cat(UpperCamelCase					,		dim=0 )
							_a      	=							torch.stack(UpperCamelCase					,		dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
							_a      	=							metric.compute(predictions=UpperCamelCase					,		references=UpperCamelCase )
							accelerator.print('''Average test metrics from all folds:'''					,		UpperCamelCase )
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							argparse.ArgumentParser(description='''Simple example of training script.''' )
							parser.add_argument(
							    '''--mixed_precision'''					,		type=UpperCamelCase					,		default=UpperCamelCase					,		choices=['''no''', '''fp16''', '''bf16''', '''fp8''']					,		help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''					,		)
							parser.add_argument('''--cpu'''					,		action='''store_true'''					,		help='''If passed, will train on the CPU.''' )
							# New Code #
							parser.add_argument('''--num_folds'''					,		type=UpperCamelCase					,		default=3					,		help='''The number of splits to perform across the dataset''' )
							_a      	=							parser.parse_args()
							_a      	=							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(UpperCamelCase					,		UpperCamelCase )


if __name__ == "__main__":
				main()





'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return "".join(chr(ord(SCREAMING_SNAKE_CASE__	) - 32	) if """a""" <= char <= """z""" else char for char in word	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




